ddalphaf.test: Test Functional DD-Classifier

In ddalpha: Depth-Based Classification and Calculation of Data Depth

Test Functional DD-Classifier

Description

Trains functional DD-classifier on the learning sequence of the data and tests it on the testing sequence.

Usage

ddalphaf.test(learn, learnlabels, test, testlabels, disc.type = c("LS", "comp"), ...)

Arguments

learn	list containing lists (functions) of two vectors of equal length, named "args" and "vals": arguments sorted in ascending order and corresponding them values respectively
learnlabels	list of output labels of the functional observations
test	the testing sequence. Has the same format as learn
disc.type	type of the used discretization scheme. "LS" for ddalphaf.train, "comp" for for compclassf.train
testlabels	list of output labels of the functinal observations
...	additional parameters passed to ddalphaf.train

Value

error	the part of incorrectly classified data
correct	the number of correctly classified objects
incorrect	the number of incorrectly classified objects
total	the number of classified objects
ignored	the number of ignored objects (outside the convex hull of the learning data)
n	the number of objects in the testing sequence
time	training time

See Also

ddalphaf.train to train the functional DD\alpha-classifier, ddalphaf.classify for classification using functonal DD\alpha-classifier, ddalphaf.getErrorRateCV and ddalphaf.getErrorRatePart to get error rate of the functional DD-classifier on particular data.

Examples

# load the fdata
df = dataf.growth()

samp = c(35:70)

ddalphaf.test(learn = df$dataf[-samp], learnlabels = df$labels[-samp], 
              test =  df$dataf[samp],  testlabels =  df$labels[samp], 
              adc.args = list(instance = "avr", 
                              numFcn = 2, 
                              numDer = 2))

ddalpha documentation built on Oct. 1, 2024, 1:07 a.m.